In [10]:
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers, models
from tensorflow.keras.applications import VGG16
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model
import tensorflow as tf
import os
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import random
import pandas as pd
import seaborn as sns
import re


train_dir = '../../T_DEV_810/dataset/train'
val_dir = '../../T_DEV_810/dataset/val'
test_dir = '../../T_DEV_810/dataset/test'
In [11]:
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

val_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary')

val_generator = val_datagen.flow_from_directory(
    val_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary')

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary')
Found 4900 images belonging to 2 classes.
Found 1050 images belonging to 2 classes.
Found 1050 images belonging to 2 classes.
In [12]:
def create_vgg16_model():
    conv_base = VGG16(weights='imagenet',
                      include_top=False,
                      input_shape=(150, 150, 3))
    
    for layer in conv_base.layers[:-4]:
        layer.trainable = False
    
    model = models.Sequential([
        conv_base,
        layers.Flatten(),
        layers.Dense(256, activation='relu'),
        layers.Dropout(0.5),
        layers.Dense(1, activation='sigmoid')
    ])
    
    model.compile(loss='binary_crossentropy',
                  optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
                  metrics=['accuracy'])
    return model
In [13]:
def plot_learning_curves(history, model_name):
    plt.figure(figsize=(12, 4))
    
    plt.subplot(1, 2, 1)
    plt.plot(history.history['accuracy'], label='Train')
    plt.plot(history.history['val_accuracy'], label='Validation')
    plt.title(f'{model_name} - Précision')
    plt.xlabel('Époque')
    plt.ylabel('Précision')
    plt.legend()
    
    plt.subplot(1, 2, 2)
    plt.plot(history.history['loss'], label='Train')
    plt.plot(history.history['val_loss'], label='Validation')
    plt.title(f'{model_name} - Perte')
    plt.xlabel('Époque')
    plt.ylabel('Perte')
    plt.legend()
    
    plt.tight_layout()
    plt.savefig(f'{model_name}_learning_curves.png')
    plt.show()
In [14]:
def get_pneumonia_type(filename):
    if "bacteria" in filename.lower():
        return "PNEUMONIA_BACTERIAL"
    elif "virus" in filename.lower():
        return "PNEUMONIA_VIRAL"
    else:
        return "PNEUMONIA_UNKNOWN"

def plot_binary_confusion_matrix(y_true, y_pred, model_name):
    cm = confusion_matrix(y_true, y_pred > 0.5)
    plt.figure(figsize=(8, 6))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', 
                xticklabels=['NORMAL', 'PNEUMONIA'], 
                yticklabels=['NORMAL', 'PNEUMONIA'])
    plt.xlabel('Prédiction')
    plt.ylabel('Vérité')
    plt.title(f'Matrice de confusion - {model_name}')
    plt.tight_layout()
    plt.savefig(f'{model_name}_confusion_matrix.png')
    plt.show()
    
    tn, fp, fn, tp = cm.ravel()
    accuracy = (tp + tn) / (tp + tn + fp + fn)
    sensitivity = tp / (tp + fn) if (tp + fn) > 0 else 0
    specificity = tn / (tn + fp) if (tn + fp) > 0 else 0
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0
    f1_score = 2 * (precision * sensitivity) / (precision + sensitivity) if (precision + sensitivity) > 0 else 0
    
    print(f"Métriques - {model_name}:")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"Sensitivity/Recall: {sensitivity:.4f}")
    print(f"Specificity: {specificity:.4f}")
    print(f"Precision: {precision:.4f}")
    print(f"F1-Score: {f1_score:.4f}")
    
    return {'accuracy': accuracy, 'sensitivity': sensitivity, 
            'specificity': specificity, 'precision': precision, 'f1_score': f1_score}
In [15]:
def plot_roc_curve(y_true, y_pred_proba, model_name):
    fpr, tpr, _ = roc_curve(y_true, y_pred_proba)
    roc_auc = auc(fpr, tpr)
    
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, lw=2, label=f'ROC (AUC = {roc_auc:.2f})')
    plt.plot([0, 1], [0, 1], 'k--', lw=2)
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('Taux de faux positifs')
    plt.ylabel('Taux de vrais positifs')
    plt.title(f'Courbe ROC - {model_name}')
    plt.legend(loc="lower right")
    plt.tight_layout()
    plt.savefig(f'{model_name}_roc_curve.png')
    plt.show()
    
    return roc_auc
In [16]:
def analyze_pneumonia_predictions(model, test_dir):
    results = {
        "NORMAL": {"correct": 0, "total": 0},
        "PNEUMONIA_BACTERIAL": {"correct": 0, "total": 0, "predicted_as_normal": 0},
        "PNEUMONIA_VIRAL": {"correct": 0, "total": 0, "predicted_as_normal": 0}
    }
    
    all_predictions = []
    
    normal_dir = os.path.join(test_dir, "NORMAL")
    if os.path.exists(normal_dir):
        for filename in os.listdir(normal_dir):
            if filename.endswith(('.png', '.jpg', '.jpeg')):
                image_path = os.path.join(normal_dir, filename)
                
                img = load_img(image_path, target_size=(150, 150))
                img_array = img_to_array(img) / 255.0
                img_array = np.expand_dims(img_array, axis=0)
                
                prediction = model.predict(img_array)[0][0]
                predicted_class = "PNEUMONIA" if prediction > 0.5 else "NORMAL"
                
                results["NORMAL"]["total"] += 1
                if predicted_class == "NORMAL":
                    results["NORMAL"]["correct"] += 1
                
                all_predictions.append({
                    "filename": filename,
                    "real_class": "NORMAL",
                    "predicted_class": predicted_class,
                    "pneumonia_probability": float(prediction),
                    "pneumonia_type": "N/A"
                })
    
    pneumonia_dir = os.path.join(test_dir, "PNEUMONIA")
    if os.path.exists(pneumonia_dir):
        for filename in os.listdir(pneumonia_dir):
            if filename.endswith(('.png', '.jpg', '.jpeg')):
                image_path = os.path.join(pneumonia_dir, filename)
                pneumonia_type = get_pneumonia_type(filename)
                
                img = load_img(image_path, target_size=(150, 150))
                img_array = img_to_array(img) / 255.0
                img_array = np.expand_dims(img_array, axis=0)
                
                prediction = model.predict(img_array)[0][0]
                predicted_class = "PNEUMONIA" if prediction > 0.5 else "NORMAL"
                
                results[pneumonia_type]["total"] += 1
                if predicted_class == "PNEUMONIA":
                    results[pneumonia_type]["correct"] += 1
                else:
                    results[pneumonia_type]["predicted_as_normal"] += 1
                
                all_predictions.append({
                    "filename": filename,
                    "real_class": "PNEUMONIA",
                    "predicted_class": predicted_class,
                    "pneumonia_probability": float(prediction),
                    "pneumonia_type": pneumonia_type
                })
    
    # Affichage des statistiques
    pneumonia_correct = results["PNEUMONIA_BACTERIAL"]["correct"] + results["PNEUMONIA_VIRAL"]["correct"]
    pneumonia_total = results["PNEUMONIA_BACTERIAL"]["total"] + results["PNEUMONIA_VIRAL"]["total"]
    pneumonia_accuracy = pneumonia_correct / pneumonia_total if pneumonia_total > 0 else 0
    
    bacterial_accuracy = results["PNEUMONIA_BACTERIAL"]["correct"] / results["PNEUMONIA_BACTERIAL"]["total"] if results["PNEUMONIA_BACTERIAL"]["total"] > 0 else 0
    viral_accuracy = results["PNEUMONIA_VIRAL"]["correct"] / results["PNEUMONIA_VIRAL"]["total"] if results["PNEUMONIA_VIRAL"]["total"] > 0 else 0
    normal_accuracy = results["NORMAL"]["correct"] / results["NORMAL"]["total"] if results["NORMAL"]["total"] > 0 else 0
    
    print("\nRésultats de détection par type de pneumonie:")
    print(f"Nombre total d'images NORMAL: {results['NORMAL']['total']}")
    print(f"Nombre total d'images PNEUMONIA: {pneumonia_total} (Bactérienne: {results['PNEUMONIA_BACTERIAL']['total']}, Virale: {results['PNEUMONIA_VIRAL']['total']})")
    print(f"Précision globale pour NORMAL: {normal_accuracy:.4f}")
    print(f"Précision globale pour PNEUMONIA: {pneumonia_accuracy:.4f}")
    print(f"Précision pour PNEUMONIA_BACTERIAL: {bacterial_accuracy:.4f}")
    print(f"Précision pour PNEUMONIA_VIRAL: {viral_accuracy:.4f}")
    
    # Graphique des précisions
    plt.figure(figsize=(10, 6))
    categories = ['NORMAL', 'PNEUMONIA (Global)', 'PNEUMONIA_BACTERIAL', 'PNEUMONIA_VIRAL']
    accuracies = [normal_accuracy, pneumonia_accuracy, bacterial_accuracy, viral_accuracy]
    plt.bar(categories, accuracies)
    plt.title('Précision de la détection par catégorie')
    plt.ylabel('Précision')
    plt.ylim([0, 1])
    for i, v in enumerate(accuracies):
        plt.text(i, v + 0.01, f"{v:.2%}", ha='center')
    plt.tight_layout()
    plt.savefig('pneumonia_type_accuracy.png')
    plt.show()
    
    # CORRECTION : Préparation des données pour la courbe ROC binaire (NORMAL vs PNEUMONIA)
    y_true_binary = []
    y_pred_proba_binary = []
    
    for pred in all_predictions:
        # Étiquette vraie : 0 pour NORMAL, 1 pour PNEUMONIA
        if pred["real_class"] == "NORMAL":
            y_true_binary.append(0)
        else:
            y_true_binary.append(1)
        
        # Probabilité de pneumonie (sortie du modèle)
        y_pred_proba_binary.append(pred["pneumonia_probability"])
    
    # Calcul et affichage de la courbe ROC corrigée
    print(f"\nNombre d'échantillons pour ROC: {len(y_true_binary)}")
    print(f"Distribution des classes - NORMAL: {sum(1 for x in y_true_binary if x == 0)}, PNEUMONIA: {sum(1 for x in y_true_binary if x == 1)}")
    
    fpr, tpr, _ = roc_curve(y_true_binary, y_pred_proba_binary)
    roc_auc = auc(fpr, tpr)
    
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, lw=2, label=f'ROC (AUC = {roc_auc:.3f})')
    plt.plot([0, 1], [0, 1], 'k--', lw=2)
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('Taux de faux positifs')
    plt.ylabel('Taux de vrais positifs')
    plt.title('Courbe ROC - Classification binaire NORMAL vs PNEUMONIA')
    plt.legend(loc="lower right")
    plt.tight_layout()
    plt.savefig('corrected_roc_curve.png')
    plt.show()
    
    # Matrice de confusion 3-classes (optionnelle)
    y_true_3class = []
    y_pred_3class = []
    
    for pred in all_predictions:
        # Classification vraie en 3 classes
        if pred["real_class"] == "NORMAL":
            actual_class = 0
        elif pred["pneumonia_type"] == "PNEUMONIA_BACTERIAL":
            actual_class = 1
        else:  # PNEUMONIA_VIRAL
            actual_class = 2
        
        # Classification prédite (limitée car le modèle ne distingue que NORMAL/PNEUMONIA)
        if pred["predicted_class"] == "NORMAL":
            predicted_class = 0
        else:  # PNEUMONIA - on ne peut pas distinguer bacterial/viral
            if pred["pneumonia_type"] == "PNEUMONIA_BACTERIAL":
                predicted_class = 1
            elif pred["pneumonia_type"] == "PNEUMONIA_VIRAL":
                predicted_class = 2
            else:
                predicted_class = 1  # Par défaut bacterial
        
        y_true_3class.append(actual_class)
        y_pred_3class.append(predicted_class)
    
    cm = confusion_matrix(y_true_3class, y_pred_3class, labels=[0, 1, 2])
    
    plt.figure(figsize=(10, 8))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', 
                xticklabels=['NORMAL', 'BACTERIAL', 'VIRAL'], 
                yticklabels=['NORMAL', 'BACTERIAL', 'VIRAL'])
    plt.xlabel('Prédiction')
    plt.ylabel('Vérité')
    plt.title('Matrice de confusion - Classification 3 classes\n(Note: Le modèle ne distingue pas bacterial/viral)')
    plt.tight_layout()
    plt.savefig('pneumonia_3class_confusion_matrix.png')
    plt.show()
    
    return results, all_predictions, roc_auc
In [17]:
def test_random_images(model, test_dir, num_images=5):
    available_classes = [d for d in os.listdir(test_dir) if os.path.isdir(os.path.join(test_dir, d))]
    
    for i in range(num_images):
        selected_class = random.choice(available_classes)
        class_dir = os.path.join(test_dir, selected_class)
        
        image_files = [f for f in os.listdir(class_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]
        if not image_files:
            continue
        
        random_image_file = random.choice(image_files)
        image_path = os.path.join(class_dir, random_image_file)
        
        img = load_img(image_path, target_size=(150, 150))
        img_array = img_to_array(img) / 255.0
        img_array = np.expand_dims(img_array, axis=0)
        
        prediction = model.predict(img_array)[0][0]
        predicted_class = "PNEUMONIA" if prediction > 0.5 else "NORMAL"
        
        pneumonia_type = "N/A"
        if selected_class == "PNEUMONIA":
            pneumonia_type = get_pneumonia_type(random_image_file)
        
        plt.figure(figsize=(10, 8))
        plt.imshow(plt.imread(image_path))
        plt.axis('off')
        
        result_text = f"Fichier: {random_image_file}\n"
        result_text += f"Classe réelle: {selected_class}\n"
        if selected_class == "PNEUMONIA":
            result_text += f"Type réel: {pneumonia_type.replace('PNEUMONIA_', '')}\n"
        result_text += f"Prédiction: {predicted_class}\n"
        result_text += f"Probabilité de pneumonie: {prediction:.2%}"
        
        plt.title(result_text, fontsize=12)
        plt.tight_layout()
        plt.savefig(f'random_image_prediction_{i}.png')
        plt.show()

4. Fonctions d'Évaluation et de Visualisation¶

📊 Visualisation des courbes d'apprentissage¶

Cette fonction permet de suivre l'évolution des performances du modèle durant l'entraînement :

def plot_learning_curves(history, model_name):
    plt.figure(figsize=(12, 4))
    
    # Courbe d'accuracy
    plt.subplot(1, 2, 1)
    plt.plot(history.history['accuracy'], label='Train')
    plt.plot(history.history['val_accuracy'], label='Validation')
    plt.title(f'{model_name} - Précision')
    plt.xlabel('Époque')
    plt.ylabel('Précision')
    plt.legend()
    
    # Courbe de loss
    plt.subplot(1, 2, 2)
    plt.plot(history.history['loss'], label='Train')
    plt.plot(history.history['val_loss'], label='Validation')
    plt.title(f'{model_name} - Perte')
    plt.xlabel('Époque')
    plt.ylabel('Perte')
    plt.legend()
    
    plt.tight_layout()
    plt.savefig(f'{model_name}_learning_curves.png')
    plt.show()

Objectifs :

  • 🎯 Suivre la convergence : Vérifier que le modèle apprend progressivement
  • 🚨 Détecter le surapprentissage : Identifier l'écart entre train et validation
  • 📈 Optimiser les hyperparamètres : Ajuster le nombre d'époques

🏷️ Classification des types de pneumonie¶

def get_pneumonia_type(filename):
    if "bacteria" in filename.lower():
        return "PNEUMONIA_BACTERIAL"
    elif "virus" in filename.lower():
        return "PNEUMONIA_VIRAL"
    else:
        return "PNEUMONIA_UNKNOWN"

Utilité : Différencier les pneumonies bactériennes des virales pour une analyse plus fine.

🔍 Matrice de confusion et métriques¶

def plot_binary_confusion_matrix(y_true, y_pred, model_name):
    cm = confusion_matrix(y_true, y_pred > 0.5)
    # ... code de visualisation ...
    
    # Calcul des métriques
    tn, fp, fn, tp = cm.ravel()
    accuracy = (tp + tn) / (tp + tn + fp + fn)
    sensitivity = tp / (tp + fn)  # Sensibilité/Rappel
    specificity = tn / (tn + fp)  # Spécificité
    precision = tp / (tp + fp)    # Précision
    f1_score = 2 * (precision * sensitivity) / (precision + sensitivity)

Métriques clés pour le diagnostic médical :

  • Sensitivity (Sensibilité) : Capacité à détecter les vrais positifs (pneumonies)
  • Specificity (Spécificité) : Capacité à identifier les vrais négatifs (cas normaux)
  • Precision : Proportion de vrais positifs parmi les cas détectés comme positifs
  • F1-Score : Harmonie entre précision et sensibilité

📈 Courbe ROC et AUC¶

def plot_roc_curve(y_true, y_pred_proba, model_name):
    fpr, tpr, _ = roc_curve(y_true, y_pred_proba)
    roc_auc = auc(fpr, tpr)
    # ... code de visualisation ...
    return roc_auc

Intérêt de la courbe ROC :

  • 🎯 Évaluation globale : Performance du modèle à tous les seuils
  • 📊 AUC (Area Under Curve) : Métrique unique de performance (0.5 = aléatoire, 1.0 = parfait)

🔬 Analyse détaillée par type de pneumonie¶

Cette fonction analyse les prédictions en distinguant les types de pneumonie :

Fonctionnalités :

  • ✅ Calcul de la précision par catégorie (Normal, Bactérienne, Virale)
  • 📊 Matrice de confusion 3-classes
  • 📈 Graphiques de précision par type

🎲 Test sur images aléatoires¶

def test_random_images(model, test_dir, num_images=5):
    # Sélection aléatoire d'images pour tester le modèle
    # Affichage des prédictions avec les images

Utilité : Validation qualitative du modèle sur des exemples concrets.


In [18]:
if __name__ == "__main__":
    model_vgg16 = create_vgg16_model()

    
    print("\nRésumé du modèle VGG16:")
    model_vgg16.summary()
    
    epochs = 10
    
    history_vgg16 = model_vgg16.fit(
        train_generator,
        epochs=epochs,
        validation_data=val_generator,
        callbacks=[
            tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True),
            tf.keras.callbacks.ReduceLROnPlateau(factor=0.2, patience=2)
        ],
        verbose=1)
    
    plot_learning_curves(history_vgg16, "VGG16")
    
    model_vgg16.save('pneumonia_detection_vgg16.h5')
    
    test_steps = test_generator.samples // test_generator.batch_size + 1
    
    print("\nÉvaluation du modèle VGG16:")
    test_generator.reset()
    vgg16_loss, vgg16_acc = model_vgg16.evaluate(test_generator, steps=test_steps)
    print(f"Précision du VGG16 sur le jeu de test: {vgg16_acc:.4f}")
    
    test_generator.reset()
    y_pred_proba_vgg16 = model_vgg16.predict(test_generator, steps=test_steps)
    y_pred_vgg16 = (y_pred_proba_vgg16 > 0.5).astype(int)
    y_true = test_generator.classes[:len(y_pred_vgg16)]
    
    metrics_vgg16 = plot_binary_confusion_matrix(y_true, y_pred_proba_vgg16, "VGG16")
    roc_auc_vgg16 = plot_roc_curve(y_true, y_pred_proba_vgg16, "VGG16")
    
    results, all_predictions, corrected_roc_auc = analyze_pneumonia_predictions(model_vgg16, test_dir)
    
    print(f"\nComparaison des AUC:")
    print(f"AUC depuis test_generator: {roc_auc_vgg16:.3f}")
    print(f"AUC corrigée: {corrected_roc_auc:.3f}")
    
    print("\nTest avec des images aléatoires:")
    test_random_images(model_vgg16, test_dir, num_images=5)
Résumé du modèle VGG16:
Model: "sequential_1"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ vgg16 (Functional)              │ (None, 4, 4, 512)      │    14,714,688 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten_1 (Flatten)             │ (None, 8192)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 256)            │     2,097,408 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 256)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ (None, 1)              │           257 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 16,812,353 (64.13 MB)
 Trainable params: 9,177,089 (35.01 MB)
 Non-trainable params: 7,635,264 (29.13 MB)
Epoch 1/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 329s 2s/step - accuracy: 0.8159 - loss: 0.3984 - val_accuracy: 0.9305 - val_loss: 0.1751 - learning_rate: 1.0000e-04
Epoch 2/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 336s 2s/step - accuracy: 0.9221 - loss: 0.2008 - val_accuracy: 0.9181 - val_loss: 0.2795 - learning_rate: 1.0000e-04
Epoch 3/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 348s 2s/step - accuracy: 0.9199 - loss: 0.2009 - val_accuracy: 0.9229 - val_loss: 0.1930 - learning_rate: 1.0000e-04
Epoch 4/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 333s 2s/step - accuracy: 0.9306 - loss: 0.1753 - val_accuracy: 0.9410 - val_loss: 0.1670 - learning_rate: 2.0000e-05
Epoch 5/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 580s 4s/step - accuracy: 0.9459 - loss: 0.1420 - val_accuracy: 0.9495 - val_loss: 0.1566 - learning_rate: 2.0000e-05
Epoch 6/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 562s 4s/step - accuracy: 0.9494 - loss: 0.1311 - val_accuracy: 0.9590 - val_loss: 0.1265 - learning_rate: 2.0000e-05
Epoch 7/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 342s 2s/step - accuracy: 0.9579 - loss: 0.1150 - val_accuracy: 0.9457 - val_loss: 0.1511 - learning_rate: 2.0000e-05
Epoch 8/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 343s 2s/step - accuracy: 0.9584 - loss: 0.1145 - val_accuracy: 0.9581 - val_loss: 0.1312 - learning_rate: 2.0000e-05
Epoch 9/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 233s 2s/step - accuracy: 0.9639 - loss: 0.0990 - val_accuracy: 0.9619 - val_loss: 0.1259 - learning_rate: 4.0000e-06
Epoch 10/10
154/154 ━━━━━━━━━━━━━━━━━━━━ 242s 2s/step - accuracy: 0.9628 - loss: 0.0965 - val_accuracy: 0.9514 - val_loss: 0.1584 - learning_rate: 4.0000e-06
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WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. 
Évaluation du modèle VGG16:
33/33 ━━━━━━━━━━━━━━━━━━━━ 28s 838ms/step - accuracy: 0.9543 - loss: 0.1348
Précision du VGG16 sur le jeu de test: 0.9533
33/33 ━━━━━━━━━━━━━━━━━━━━ 29s 857ms/step
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Métriques - VGG16:
Accuracy: 0.4905
Sensitivity/Recall: 0.4610
Specificity: 0.5200
Precision: 0.4899
F1-Score: 0.4750
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Résultats de détection par type de pneumonie:
Nombre total d'images NORMAL: 525
Nombre total d'images PNEUMONIA: 525 (Bactérienne: 338, Virale: 187)
Précision globale pour NORMAL: 0.9829
Précision globale pour PNEUMONIA: 0.9238
Précision pour PNEUMONIA_BACTERIAL: 0.9438
Précision pour PNEUMONIA_VIRAL: 0.8877
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Nombre d'échantillons pour ROC: 1050
Distribution des classes - NORMAL: 525, PNEUMONIA: 525
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Comparaison des AUC:
AUC depuis test_generator: 0.476
AUC corrigée: 0.992

Test avec des images aléatoires:
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 150ms/step
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5. Entraînement et Évaluation du Modèle VGG16¶

🚀 Configuration de l'entraînement¶

if __name__ == "__main__":
    # Création du modèle VGG16
    model_vgg16 = create_vgg16_model()
    
    print("\nRésumé du modèle VGG16:")
    model_vgg16.summary()
    
    # Entraînement du modèle
    epochs = 5
    
    # Entraînement du VGG16
    history_vgg16 = model_vgg16.fit(
        train_generator,
        epochs=epochs,
        validation_data=val_generator,
        callbacks=[
            tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True),
            tf.keras.callbacks.ReduceLROnPlateau(factor=0.2, patience=2)
        ],
        verbose=1)

📋 Callbacks utilisés¶

Callback Fonction Paramètres
EarlyStopping Arrêt anticipé si pas d'amélioration patience=3, restore_best_weights=True
ReduceLROnPlateau Réduction du learning rate factor=0.2, patience=2

Avantages :

  • 🛡️ Protection contre le surapprentissage
  • ⚡ Optimisation automatique du learning rate
  • 💾 Conservation des meilleurs poids

📊 Pipeline d'évaluation complète¶

# Évaluation sur le jeu de test
test_steps = test_generator.samples // test_generator.batch_size + 1

# Évaluation du VGG16
print("\nÉvaluation du modèle VGG16:")
test_generator.reset()
vgg16_loss, vgg16_acc = model_vgg16.evaluate(test_generator, steps=test_steps)

# Obtenir les prédictions et les vraies étiquettes
test_generator.reset()
y_pred_proba_vgg16 = model_vgg16.predict(test_generator, steps=test_steps)
y_pred_vgg16 = (y_pred_proba_vgg16 > 0.5).astype(int)
y_true = test_generator.classes[:len(y_pred_vgg16)]

# Visualisations et analyses
plot_learning_curves(history_vgg16, "VGG16")
metrics_vgg16 = plot_binary_confusion_matrix(y_true, y_pred_proba_vgg16, "VGG16")
roc_auc_vgg16 = plot_roc_curve(y_true, y_pred_proba_vgg16, "VGG16")
results, all_predictions = analyze_pneumonia_predictions(model_vgg16, test_dir)
test_random_images(model_vgg16, test_dir, num_images=5)

💾 Sauvegarde et persistance¶

# Sauvegarde du modèle
model_vgg16.save('pneumonia_detection_vgg16.h5')

Important : Le modèle est sauvegardé pour utilisation future sans réentraînement.

🎯 Workflow d'évaluation¶

  1. Entraînement avec callbacks pour optimisation automatique
  2. Visualisation des courbes d'apprentissage
  3. Évaluation quantitative avec métriques multiples
  4. Analyse qualitative avec images aléatoires
  5. Sauvegarde du modèle entraîné

💡 Note : Cette approche méthodique garantit une évaluation complète et reproductible du modèle pour la classification de radiographies thoraciques.